Aircraft employ enhanced vision systems (singularly, EVS) to provide the pilot a real-time view of a scene outside an aircraft. Unlike synthetic vision systems which provide the pilot with colorful synthetic images based upon the aircraft's position measured by a navigation system for finding latitude, longitude, and altitude and a database of Earth's elevations from which elevations may be retrieved knowing the aircraft's position, the EVS employs one or more sensors (e.g., a camera to detect lighted objects visible to the human eye) from which images may be formed. As such, a navigation system and a database are not needed when an EVS is employed.
Generally, EVS sensors acquire wavelengths in the electromagnetic spectrum from which spatial images comprised of a collection of pixels may be formed and presented to the viewer. While the spatial pixel images being presented to the viewer as changes to pixel intensities of colors or shades is useful by providing the viewer with an image of an actual scene visually perceptible to a human eye, spatial pixel images may be converted into frequency pixel images comprised of frequency amplitudes from which useful information imperceptible to the human eye may be gleaned by measuring changes in frequency pixel amplitudes with the use of data image processing techniques.
The process of conversion in image processing could employ pyramids that produce multiple layers, where the size of each subsequent layer is half of the size (or resolution) of the layer from which from which the subsequent layer is formed. If each subsequent layer is stacked upon the preceding layer, the general shape of a pyramid forms. One possible conversion technique which deconstructs a spatial image into a frequency image is known as the Laplacian pyramid.
Referring now to FIG. 1A, an exemplar spatial pixel image containing a runway, terminal, road, and bridge built upon flat terrain has been deconstructed into a frequency pixel image comprised of a plurality of layers of a Laplacian pyramid (the beginning of an upside-down pyramid comprised of two layers is formed). For each layer, one or more edge detection techniques may be employed to identify points in a digital layer at which the image brightness changes sharply, indicative of discontinuities referred to as “edges” of features. The changes in brightness of Laplacian layer pixels may be measured as pixel amplitude. Referring now to FIG. 1B, one row of the top Laplacian layer (referred to as layer 0 of FIG. 1A) is highlighted along with a graph containing a plot of frequency amplitudes versus pixel columns from which edges of features of the runway (identified with the letters A, B, C, and D) and highway (identified with the letters E and F) for one row may be identified. As observed, few features found on flat terrain may produce easily definable edges.
While a scene comprised of flat terrain and few features may produce easily definable edges, a scene which contains many objects, uneven or jagged terrain may produce numerous edges measured by numerous pixel amplitudes referred to as “noise.” With the presence of numerous edges, it may be difficult to separate or distinguish between content comprised of “features” and content comprised of “noise.” Without an employment of enhancement methods to manipulate the frequency pixel image, a reconstruction of the Laplacian layers may result with no changes being made, resulting in the same spatial image.